Te-PID: An Adaptive Erasure Coding Temperature Management System for Optimized Cloud Storage

Cloud storage systems widely adopt temperature-based erasure coding (EC) formats to balance data access efficiency and storage costs. These systems categorize data into hot and cold EC formats based on access frequency, allocating frequently accessed data to hot EC and less accessed data to cold EC. However, achieving an optimal balance between storage costs with different EC formats and access efficiency through effective data categorization remains a significant challenge due to the dynamic nature of data access patterns. Existing static, rule-based methods lack predictive capability, fail to consider the global system state, and cannot dynamically adapt to changing environments. To address these limitations, we propose Te-PID (Temperature Proportional-Integral-Derivative), an adaptive EC temperature management system that optimizes data storage by integrating individual access behaviors with global system patterns. Te-PID consists of three components: a prediction model to forecast traffic patterns and anticipate storage needs, a filter module to minimize unnecessary EC format transitions, and a PID controller to maintain the cold traffic ratio within predefined constraints, balancing storage costs and performance. Experiments on Microsoft Azure's XTemperature system, including a 30-day real-world deployment across 830 tenants, demonstrate that Te-PID achieves a 30.1% improvement in storage savings compared to existing methods, while maintaining access efficiency and adhering to system constraint.